Algorithm Fusion
Combining the results from multiple submissions sometimes yields improved accuracy over individual submissions. In this section score-level fusion is used to combine search results from multiple submissions. Equal-weighted Neyman-Pearson fusion is used to merge candidate lists from different submissions into a single consolidated candidate list. The dissimilarity score associated with each candidate is normalized prior to fusion (see LFAR score). This normalized score is a measure of similarity rather than dissimilarity. Any candidate appearing on multiple lists is assigned a single fused score by summing the the individual LFAR scores. The merged candidate list is then reordered by the LFAR scores.
Only fusion results that yield an improvement in accuracy over the individual submissions are shown.
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